Future Enterprise Systems September 20th, 2010
Ozelin López, Katharina Siorpaes www.playence.com
Enterprise Mul-media Integra-on and Search
MoAvaAon • With increasing bandwidth, cheaper storage of data, and improved hardware, mulAmedia content is gaining importance.
• 40% of worldwide traffic in Internet in 2010 will be consumed by viewing and/or downloading videos
• In 2014, the sum of all video forms will consume 90% of traffic
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Source: Cisco
MoAvaAon
• As rich medium, video can transport and conserve more informaAon than text ever could.
• This type of content also creates new issues with respect to search, integraAon, management and preservaAon
• A new challenge arises, when trying to integrate text with mulAmedia
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MoAvaAon
• So far, only small parts of data comes out of automaAc analysis on mulAmedia assets
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• The work is finally done, mostly, by humans.
• MulAmedia informaAon is isolated
MulAmedia HolisAc View in the Enterprise
• Knowledge AcquisiAon process is criAcal to any semanAcally enhanced system
• Current informaAon systems can only rely on weak annotaAon processes for mulAmedia assets
• When needed, manual annotaAon tagging is performed with the help of shared vocabularies and thesauri.
• The level of integraAon with exisAng InformaAon Systems is limited
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MulAmedia HolisAc View in the Enterprise
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• Interlinking
• AnnotaAon
• IntegraAon
• Search
• Management
AnnotaAon Process
• The annotaAon process can be done automaAcally by the system or manually. The accuracy and precision of this process depends on the type of content.
• Textual annotaAon is done automaAcally, using Ontologies and NLP techniques to pinpoint textual references to concepts and instances in the source.
• Several domain ontologies can be used to this purpose, providing a mulA-‐view perspecAve on the same resource.
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AnnotaAon Process
• In the case of video or audio, automaAc analysis has less accuracy.
• ASR can done the work up to some extent (60-‐80%)
• For video or image analysis, high-‐level features can be obtained, like face detecAon, detecAon of objects, daylight classificaAon and other basic features.
• In these cases, collaboraAon human annotaAon must be supported.
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AnnotaAon Process
• In the case of video or audio, automaAc analysis has less accuracy.
• ASR can done the work up to some extent (60-‐80%)
• For video or image analysis, high-‐level features can be obtained, like face detecAon, detecAon of objects, daylight classificaAon and other basic features.
• In these cases, collaboraAon human annotaAon must be supported.
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IntegraAon
• AnnotaAon process will leave a set of resources linked to the same semanAc content
• Data can easily be located, mashed-‐up and displayed regardless its original source.
• IntegraAon in playence Media empowers the user to locate a meeAng recording and being able to find related videos, audios, pictures and documents.
• Once annotated, everything can be queried using the same model.
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IntegraAon
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Search
• playence Media performs semanAc search, using annotaAons and applying faceted search and semanAc navigaAon to narrow the set of results
• When searching, playence Media makes use of Natural Language Processing techniques like lemmaAzaAon or spell check.
• SemanAc features are used in query expansion, like synonym expansion through SKOS, or generalizaAon-‐specializaAon expansion, using the “is-‐a” relaAonship and instances from concepts involved, or using more complex relaAons in query expansion.
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Search
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Intelligent mulAmedia management including: automaAc semanAc annotaAon, interlinking, in-‐video search and browsing.
• Automa-c mul--‐language knowledge extrac-on – LocaAon of relevant key-‐words and domain
knowledge through batch ASR. – Efficient video annotaAon for later use.
• In-‐video Search – Land a search at the exact second where the
relevant content is being played. – Land a search at the exact second where the
relevant actor is talking. • Mul-linguality
– Support for a wide variety of languages. – Complex cross-‐language query and mulAmedia
asset retrieval. • Mul-format support
– AVI, MPEG, FLV, etc.
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playence Media
• Dynamic ra-ng – On-‐the-‐go signalling on the most interesAng
porAons of a rich media asset. – Beher locaAon of releant content and improve
search results. • Manual annota-on refinement
– Refinement of automaAc annotaAons. – AddiAon of annotaAons.
• Browsing and asset naviga-on – Powerful browsing engine to keep sight of
huge amounts of media content. – Eases finding, discovering and accessing the
required media resources. • Rela-onship viewer
– Snapshot view of a porAon of the subjacent data model.
– Document set filtering by way of query expansion and reducAon.
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playence Media
Further Steps
• The challenges associated with mulAmedia integraAon in the enterprise are manifold
• Relevance and in-‐video search • Ontology evoluAon from customer perspecAve • Workflow and collaboraAve processes are needed.
• Interlinking – Linked Open Data – Linked Closed Data
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Conclusion
• Media is going to be the next enterprise communicaAon mechanism
• Media is a bitch! – We need completely new ways of manage it – Business is beyond text, but technology is not
• Providing a holisAc view empowers enterprises to manage mulAmedia assets
• This holisAc view comprises heavily informaAon related processes: annotaAon, integraAon, search, interlinking
• playence Media comes to the playground to help companies dealing with these challenges.
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www.playence.com
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